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arxiv: 2604.17669 · v2 · pith:PQKF7GUXnew · submitted 2026-04-19 · 💻 cs.CV

Low Light Image Enhancement Challenge at NTIRE 2026

George Ciubotariu , Sharif S M A , Abdur Rehman , Fayaz Ali Dharejo , Rizwan Ali Naqvi , Marcos V. Conde , Radu Timofte , Zhi Jin
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Hongjun Wu Wenjian Zhang Chang Ye Xunpeng Yi Qinglong Yan Yibing Zhang Zaynab Ali Saiprasad Meesiyawar Varda I Pattanshetty Varsha I Pattanshetty Nikhil Akalwadi Padmashree Desai Ramesh Ashok Tabib Uma Mudenagudi Hao Yang Ruikun Zhang Liyuan Pan Furkan K{\i}nl{\i} Donghun Ryou Inju Ha Junoh Kang Bohyung Han Wei Zhou Yuval Haitman Ariel Lapid Reuven Peretz Idit Diamant Leilei Cao Shuo Zhang Praful Hambarde Prateek Shaily Jayant Kumar Hardik Sharma Aashish Negi Sachin Chaudhary Akshay Dudhane Amit Shukla MoHao Wu Lin Wang Jiachen Tu Guoyi Xu Yaoxin Jiang Jiajia Liu Yaokun Shi Raul Balmez Alexandru Brateanu Ciprian Orhei Cosmin Ancuti Codruta O. Ancuti Bilel Benjdira Anas M. Ali Wadii Boulila Kaifan Qiao Bofei Chen Jingyi Xu Duo Zhang Xin Deng Mai Xu Shengxi Li Lai Jiang Harini A Ananya N Lakshanya K Ying Xu Xinyi Zhu Shijun Shi Jiangning Zhang Yong Liu Kai Hu Jing Xu Xianfang Zeng Jinao Song Guangsheng Tang Cheng Li Yuqiang Yang Ziyi Wang Yan Chen Long Bao Heng Sun Mohab Kishawy Jun Chen Wan-Chi Siu Yihao Cheng Hon Man Hammond Lee Chun-Chuen Hui
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Pith reviewed 2026-05-19 18:18 UTC · model grok-4.3

classification 💻 cs.CV
keywords low-light image enhancementNTIRE challengeimage denoisingimage restorationneural networkscomputer visiondatasetcompetition
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The pith

The NTIRE 2026 Low Light Image Enhancement Challenge shows clear progress in restoring details from low-contrast and noisy images via 22 submitted networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper reviews the NTIRE 2026 Low Light Image Enhancement Challenge, which drew 195 registrations for the first track and 153 for the second, resulting in 22 valid team submissions. It evaluates the proposed networks on a novel dataset to measure how well they recover lost visual information under challenging low-light conditions. The review documents advances in techniques that handle both enhancement and denoising together. A reader would care because such methods directly affect real-world uses like night photography, security cameras, and medical imaging where poor lighting degrades image quality.

Core claim

The paper establishes that the submitted solutions in the NTIRE 2026 challenge achieve meaningful gains in producing clearer images from low-light inputs by learning representative visual cues to compensate for contrast loss and noise, as demonstrated through evaluation on the authors' new dataset.

What carries the argument

The novel low-light dataset paired with the 22 submitted neural networks that perform joint enhancement and denoising.

Load-bearing premise

The 22 submitted entries together with the novel dataset give a representative view of current capabilities in low-light enhancement.

What would settle it

New methods that clearly outperform all 22 entries when tested on the same dataset, or independent tests showing the dataset misses common real-world low-light degradations.

Figures

Figures reproduced from arXiv: 2604.17669 by Aashish Negi, Abdur Rehman, Akshay Dudhane, Alexandru Brateanu, Amit Shukla, Ananya N, Anas M. Ali, Ariel Lapid, Bilel Benjdira, Bofei Chen, Bohyung Han, Chang Ye, Cheng Li, Chun-Chuen Hui, Ciprian Orhei, Codruta O. Ancuti, Cosmin Ancuti, Donghun Ryou, Duo Zhang, Fayaz Ali Dharejo, Furkan K{\i}nl{\i}, George Ciubotariu, Guangsheng Tang, Guoyi Xu, Hao Yang, Hardik Sharma, Harini A, Heng Sun, Hongjun Wu, Hon Man Hammond Lee, Idit Diamant, Inju Ha, Jayant Kumar, Jiachen Tu, Jiajia Liu, Jiangning Zhang, Jinao Song, Jing Xu, Jingyi Xu, Jun Chen, Junoh Kang, Kaifan Qiao, Kai Hu, Lai Jiang, Lakshanya K, Leilei Cao, Lin Wang, Liyuan Pan, Long Bao, Mai Xu, Marcos V. Conde, Mohab Kishawy, MoHao Wu, Nikhil Akalwadi, Padmashree Desai, Praful Hambarde, Prateek Shaily, Qinglong Yan, Radu Timofte, Ramesh Ashok Tabib, Raul Balmez, Reuven Peretz, Rizwan Ali Naqvi, Ruikun Zhang, Sachin Chaudhary, Saiprasad Meesiyawar, Sharif S M A, Shengxi Li, Shijun Shi, Shuo Zhang, Uma Mudenagudi, Varda I Pattanshetty, Varsha I Pattanshetty, Wadii Boulila, Wan-Chi Siu, Wei Zhou, Wenjian Zhang, Xianfang Zeng, Xin Deng, Xinyi Zhu, Xunpeng Yi, Yan Chen, Yaokun Shi, Yaoxin Jiang, Yibing Zhang, Yihao Cheng, Ying Xu, Yong Liu, Yuqiang Yang, Yuval Haitman, Zaynab Ali, Zhi Jin, Ziyi Wang.

Figure 1
Figure 1. Figure 1: Pipeline of RLLIE proposed by SYSU-FVL WHU-MVP Training Details. They utilize the full-resolution images from the LSD dataset provided by the organizers, rather than the cropped patch version. Training is performed ex￾clusively on four NVIDIA RTX 4090 GPUs. A progres￾sive training strategy is adopted, where the patch sizes are set to 960, 1280, 1280, 1440, 1600, with correspond￾ing batch sizes of 4, 2, 2, … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed UHDM architecture by [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: BITssvgg’s architecture overview inference, the restored output is resized back to the original resolution. The result is then clipped to the range of [0, 1] and saved as the final output image. BAU-Vision Training Details. To stabilize training on given challenge dataset, They define a sample-adaptive coefficient vector α ∈ R N for each batch of N samples. For the i-th sam￾ple, the coefficient αi is compu… view at source ↗
Figure 4
Figure 4. Figure 4: BAU-Vision’s Wave-P architecture Input Image CIDNet+ ( full Image ) OSEDiff ( patch 2048 ) Laplacian Pyramid Fusion ORNet Enhanced Image Global Condition low-freq (tone) high-freq (detail) down sampling [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: SNUCV’s MB-LPFR pipeline SNUCV Training Details. They trained CIDNet+ [97] and OSED￾iff [93] exclusively on the LSD training dataset [72]. For CIDNet+, they followed the original paper’s protocol and cropped patches to a size of 1280 × 1280. OSEDiff was initialized from a pre-trained Stable Diffusion checkpoint (CompVis/stable-diffusion-v1-4) and fine-tuned on the LSD dataset according to the ICM-SR [36] t… view at source ↗
Figure 7
Figure 7. Figure 7: AAIR ARM’s LFM-LLIE overview. (a) Training: A flow-matching network uθ (HDiT-based) is trained in latent space using interpolated samples zt created from the noise-perturbed low-light latent image z˜1. (b) Inference: Starting from z1 = E(x1), the latent is iteratively transformed by the learned veloc￾ity field uθ into zˆ0 and decoded by D to produce the enhanced image xˆ0. TranssionAI [PITH_FULL_IMAGE:fig… view at source ↗
Figure 8
Figure 8. Figure 8: TranssionAI overview Training Details. They optimize the model using a hy￾brid loss function that combines reconstruction, perceptual, structural, and frequency-domain constraints: L = 0.2Lr + 0.2Llc +Llpips + 0.2Lgrad + 0.5Lf req (12) where Lr is the mean intensity reconstruction loss (GT￾Mean loss was adopted, instead of L1 loss), and Llc de￾notes the luminance and chrominance loss adopted from the basel… view at source ↗
Figure 9
Figure 9. Figure 9: Overview of the proposed weighted late-fusion architecture by [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: DH-XHDL-Team’s TEI-LLIE architechture of structural detail. They use AdamW (β1=0.9, β2=0.999, weight decay 10−3 ) with an initial learning rate of 10−4 and cosine annealing over 300 scheduled epochs. An expo￾nential moving average (EMA) of the weights is maintained with a decay of 0.999. Data augmentation includes random horizontal and vertical flips and 90◦ rotations. Training was conducted on 2× NVIDIA … view at source ↗
Figure 11
Figure 11. Figure 11: DUSKAN architecture by PSU. Top: Symmetric 4-level U-Net with DUSKANBlock stages, strided downsampling, PixelShuf￾fle upsampling, and global residual learning. Bottom: DUSKANBlock detail. Path A (blue) extracts global features via FFT magnitude modulation and fuses them with local multi-scale depthwise convolution features. Path B (red) uses Kolmogorov-Arnold polynomial-basis activations with a parallel-a… view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative comparison of DNDiff’s color performance [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: DNATT pipeline of Team Lucky one. Dense partitioning into many small overlapping tiles. Our uniform 2x3 tiling scheme [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: APRIL-AIGC’s comparison of tiling strategies for high￾resolution inference. A fixed 2×3 partition with moderate overlap yields fewer illumination discontinuities than denser window lay￾outs. 1600 × 1456 pixels with overlaps close to 128 × 144 pix￾els. They use 5 sampling steps, a guidance scale of 2.0, and an empty string negative prompt. In practice, this setting provides the most stable trade-off betwee… view at source ↗
Figure 15
Figure 15. Figure 15: MiVideoDLLIE’s pipeline of MiDLLIE Training Details. During the training phase, to improve training efficiency while preserving image high-frequency details as much as possible, they resize the training data from 512 × 512 to 256 × 256. The optimizer is Adam with hyperparameters β1 = 0.9 and β2 = 0.99, and the initial learning rate is set to 1 × 10−4 , using a cosine annealing with restarts strategy. For … view at source ↗
Figure 16
Figure 16. Figure 16: RetinexDualV2 overview [PITH_FULL_IMAGE:figures/full_fig_p024_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Overall structure of WIRNet to recover fine details. To address these limitations, they introduce three key contributions: 24 [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
read the original abstract

This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions by learning representative visual cues with the purpose of restoring information loss due to low-contrast and noisy images. A total of 195 participants registered for the first track and 153 for the second track of the competition, and 22 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in (joint denoising and) low-light image enhancement, showcasing the significant progress in the field, while leveraging samples of our novel dataset.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript reports on the NTIRE 2026 Low Light Image Enhancement Challenge, stating that 195 participants registered for track 1 and 153 for track 2, with 22 teams submitting valid entries. It claims to present a comprehensive review of proposed solutions and final results while thoroughly evaluating state-of-the-art advances in low-light (and joint denoising) image enhancement and demonstrating significant progress via a novel dataset.

Significance. A well-documented challenge report with quantitative rankings, method summaries, and bias checks could usefully record community progress on low-light enhancement; the current text provides registration/submission counts but little evidence that the 22 entries or dataset yield a representative or unbiased assessment of capabilities.

major comments (2)
  1. Abstract: the assertion that the paper 'thoroughly evaluates the state-of-the-art advances ... showcasing the significant progress' is unsupported, as the text supplies only registration (195/153) and submission (22) counts without performance metrics, rankings, error analysis, or comparisons to prior challenges or external baselines on the same test set.
  2. Challenge setup / results sections: no quantitative verification of the evaluation protocol (e.g., overfitting checks, noise-distribution statistics, or cross-dataset transfer) is described, leaving the claim that the novel dataset plus self-selected entries give a representative picture of current capabilities untested.
minor comments (1)
  1. Abstract: the parenthetical '(joint denoising and)' is unclear; specify whether the two tracks are separate or joint and how this affects the reported progress.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript reporting the NTIRE 2026 Low Light Image Enhancement Challenge. We have revised the abstract for greater precision and strengthened the challenge setup and results sections with additional quantitative details on the evaluation protocol.

read point-by-point responses

  1. Referee: Abstract: the assertion that the paper 'thoroughly evaluates the state-of-the-art advances ... showcasing the significant progress' is unsupported, as the text supplies only registration (195/153) and submission (22) counts without performance metrics, rankings, error analysis, or comparisons to prior challenges or external baselines on the same test set.

    Authors: We acknowledge that the original abstract wording could overstate the scope of analysis provided. The manuscript includes a dedicated results section presenting quantitative rankings, PSNR and SSIM metrics for all 22 valid submissions, method summaries, and qualitative comparisons. To address the concern directly, we have revised the abstract to state that the paper presents the challenge outcomes and reviews participant solutions, highlighting progress observed in this setting. We have also added brief comparisons to prior low-light challenges in the introduction for context. revision: yes

  2. Referee: Challenge setup / results sections: no quantitative verification of the evaluation protocol (e.g., overfitting checks, noise-distribution statistics, or cross-dataset transfer) is described, leaving the claim that the novel dataset plus self-selected entries give a representative picture of current capabilities untested.

    Authors: We agree that explicit verification details strengthen the paper. The revised manuscript now reports noise-distribution statistics computed on the dataset, describes the validation-set monitoring used during the challenge to check for overfitting, and includes consistency analysis across test subsets. Cross-dataset transfer experiments were outside the challenge scope, which focused on the new dataset; we have noted this limitation and its implications for broader generalizability. These additions better substantiate the evaluation protocol and the challenge's contribution to assessing current capabilities. revision: partial

Circularity Check

0 steps flagged

No circularity: standard challenge report with external submissions

full rationale

The paper is a competition summary that registers participant numbers (195/153), reports 22 valid external team submissions, and evaluates results on a novel dataset. No equations, derivations, fitted parameters, or predictions appear in the provided text. The central claim of 'significant progress' and 'thorough evaluation' rests on the independent submissions and dataset rather than any self-referential construction, self-citation chain, or renaming of prior results. This matches the expected non-finding for descriptive challenge papers without mathematical load-bearing steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The report rests on the existence of a novel dataset and the validity of the submitted solutions; no free parameters, mathematical axioms, or invented entities are described in the abstract.

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